Method And Arrangement For Noise Floor Estimation

ABSTRACT

A method of enabling improved soft noise power floor estimation in a code division multiple access wireless communication system, measuring (SO) samples of a received total wideband power, estimating (S 1 ) a probability distribution for a first power quantity from the measured received total wideband power, estimating (S 2 ) a mean power level for the first power quantity and adapting (S 3 ) the width of the probability density function based on the estimated mean power level to enable computation of a probability density function of a noise floor measure that is a discretization on a grid.

TECHNICAL FIELD

The present invention relates in general to methods and devices for loadestimation in cellular communications systems and in particular toimproved noise-floor estimation in wideband code division multipleaccess communication systems.

BACKGROUND

Wideband Code Division Multiple Access (WCDMA) telecommunication systemshave many attractive properties that can be used for future developmentof telecommunication services. A specific technical challenge in e.g.WCDMA and similar systems is the scheduling of enhanced uplink channelsto time intervals where the interference conditions are favorable, andwhere there exist a sufficient capacity in the uplink of the cell inquestion to support enhanced uplink channels. It is well known thatexisting users of the cell all contribute to the interference level inthe uplink of WCDMA systems. Further, terminals in neighbor cells alsocontribute to the same interference level. This is because all users andcommon channels of a cell transmit in the same frequency band when CodeDivision Multiple Access (CDMA) technology is used. Consequently, theload of the cell is directly related to the interference level of thesame cell.

In order to retain stability of a cell, the load needs to be kept belowa certain level. Several radio resource management (RRM) algorithms suchas scheduling and admission control rely on accurate estimates of theuplink load. This follows since the majority of uplink user channels, atleast in WCDMA, are subject to power control. This power control and RRMalgorithms aim at keeping the received power level of each channel at acertain signal to interference ratio (SIR), in order to be able to meetspecific service requirements. This SIR level is normally such that thereceived powers in the radio base station (RBS) are several dBs belowthe interference level. De-spreading in so-called RAKE-receivers thenenhance each channel to a signal level where the transmitted bits can befurther processed, e.g. by channel decoders and speech codecs that arelocated later in the signal processing chain. The reader is referred to[1] for further details.

Since the RBS tries to keep each channel at its specific preferred SIRvalue, it may happen that an additional user, or bursty data traffic ofan existing user, raises the interference level, thereby momentarilyreducing the SIR for the other users. The response of the RBS is tocommand a power increase to all other users, something that increasesthe interference even more. Normally this process remains stable below acertain load level. In case a high capacity channel would suddenlyappear, the raise in the interference becomes large and the risk forinstability, a so-called power rush, increases. This explains why it isa necessity to schedule high capacity uplink channels. like the enhanceduplink (E-UL) channel in WCDMA, so that one can insure that instabilityis avoided. In order to do so, the momentary load must be estimated inthe RBS. This enables the assessment of the capacity margin that is leftto the instability point.

The load of a cell in e.g. a CDMA system is usually referred to somequantity related to power, typically noise rise. A number of noise risemeasures do exist. The most important one is perhaps the Rise overThermal (RoT) that is defined as the quotient of the total interferenceof the cell and the thermal noise power floor of the receiver of theRBS. Other measures include e.g. in-band non-WCDMA interference withrespect to the thermal noise floor. Consequently, power quantities, suchas total power level and noise floor (ideally thermal noise floor), haveto be determined. Determinations of noise floor are typically associatedwith relatively large uncertainties, which even may be in the same orderof magnitude as the entire available capacity margin. This isparticularly true when only measurements of total received power areavailable. It will thus be very difficult indeed to implement e.g.enhanced uplink channel functionality without improving the loadestimation connected thereto.

It could furthermore be mentioned that an equally important parameterthat requires load estimation for its control, is the coverage of thecell. The coverage is normally related to a specific service that needsto operate at a specific SIR to function normally. The uplink cellboundary is then defined by a terminal that operates at maximum outputpower. The maximum received channel power in the RBS is defined by themaximum power of the terminal and the path loss to the digital receiver.Since the path-loss is a direct function of the distance between theterminal and the RBS, a maximum distance from the RBS results. Thisdistance, taken in all directions from the RBS, defines the coverage.

It now follows that any increase of the interference level results in areduced SIR that cannot be compensated for by an increased terminalpower. Consequently, the path loss needs to be reduced to maintain theservice. This means that the terminal needs to move closer to the RBS,i.e. the coverage of the cell is reduced.

From the above discussion, it is clear that in order to maintain thecell coverage that the operator has planned for, it is necessary to keepthe interference below a specific level. This means that load estimationis important also for coverage. In particular, load estimation isimportant from a coverage point of view in the fast scheduling ofenhanced uplink traffic in the RBS. Furthermore, the admission controland congestion control functionality in the radio network controller(RNC) that controls a number of RBSs also benefits from accurateinformation on the momentary noise rise of the cell.

All above mentioned noise rise measures have in common that they rely onaccurate estimates of the background noise. Therefore, there is a needfor methods and arrangements for providing efficient and accurate realtime estimates for the background noise.

SUMMARY

A general problem with prior art CDMA communications networks is thatload estimations are presented with an accuracy which makes careful loadcontrol difficult. In particular, determination of noise rise suffersfrom significant uncertainties, primarily caused by difficulties toestimate the noise floor.

A general object of the present invention is to provide improved methodsand arrangements for determining power-related quantities, e.g. loadestimation.

A further object of the present invention is to provide methods andarrangements giving opportunities for more accurate determination ofnoise related quantities, e.g. noise floor power estimates.

A specific object of the present invention is to provide a methodenabling a soft noise floor estimation that follows a mean power level.

These and other objects are achieved in accordance with the attached setof claims.

According to a basic aspect the invention comprises a method of enablingimproved soft noise power floor estimation in a code division multipleaccess telecommunication system, wherein a received total wideband poweris measured in a first step S0, subsequently a probability function fora first power quantity is estimated S1 based on the measured receivedtotal wideband power, a mean power level for the first power quantity isestimated S2 and finally the width of the estimated probability densityfunction is adapted S3 based on the estimated mean power level to enablesubsequent computation of a probability density function of a noisefloor measure that is defined on a discretization on a grid.

An advantage of the present invention comprises enabling accurate realtime noise floor estimation.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, together with further objects and advantages thereof, maybest be understood by referring to the following description takentogether with the accompanying drawings, in which:

FIG. 1 is a schematic of a telecommunication system in which the presentinvention is applicable;

FIG. 2 is a schematic block diagram of a method in which the presentinvention is applicable:

FIG. 3 is another schematic block diagram of a method in which thepresent invention is applicable;

FIG. 4 is a graph of the power grid points vs. power in the lineardomain;

FIG. 5 is a graph of a typical discretized probability density functionvs. linear power;

FIG. 6 is a schematic flow diagram of an embodiment of a methodaccording to the present invention;

FIG. 7 illustrates the measured RTWP vs. time;

FIG. 8 illustrates the estimated noise power floor of a known method;

FIG. 9 illustrates the estimated noise power floor according to thepresent invention.

FIG. 10 is a schematic illustration of an embodiment of a systemaccording to the invention.

ABBREVIATIONS

E-UL Enhanced Up Link

RBS Radio Base Station

RNC Radio Network Controller

RoT Rise over Thermal

RRM Radio Resource Management

RSSI Received Signal Strength Indication

RTWP Received Total Wideband Power

SIR Signal to Interference Ratio

WCDMA Wideband Code Division Multiple Access

DETAILED DESCRIPTION

The following detailed description is introduced by a somewhat deeperdiscussion about how to perform noise floor estimation and the problemsencountered by a plurality of possible solutions, in order to reveal theseriousness thereof. This is done in the context of but not limited to atypical Wideband Code Division Multiple Access (WCDMA) system; the ideasof the present invention are equally applicable to many types ofcellular systems where accurate noise floor power estimations arenecessary but difficult to provide.

Reference and Measurement Points

In a typical signal chain of a radio base station (RBS) a receivedwideband signal from an antenna first passes an analogue signalconditioning chain, which consists of cables, filters etc. Variationsamong components together with temperature drift, render the scalefactor of this part of the system to be undetermined with about 2-3 dBs,when the signal enters a receiver. This is discussed further below. Inthe receiver, a number of operations take place. For load estimation itis normally assumed that a total received wideband power (RTWP) ismeasured at some stage. Furthermore, it is assumed in this descriptionthat code power measurements, i.e. powers of each individualchannel/user of the cell, are made available at another stage.

There are several reasons for the difficulties to estimate the thermalnoise floor power. One reason as indicated above is that the thermalnoise floor power, as well as the other received powers, is affected bycomponent uncertainties in the analogue receiver front end. The signalreference points are, by definition, at the antenna connector. Themeasurements are however obtained after the analogue signal conditioningchain, in the digital receiver. These uncertainties also possess athermal drift.

The analogue signal conditioning electronics chain does introduce ascale factor error of 2-3 dB between RBSs (batch) that is difficult tocompensate for. The RTWP measurement that is divided by the defaultvalue of the thermal noise power floor may therefore be inconsistentwith the assumed thermal noise power floor by 2-3 dB. The effect wouldbe a noise rise estimate that is also wrong by 2-3 dB. Considering thefact that the allowed noise rise interval in a WCDMA system is typically0-7 dB, an error of 2-3 dB is not acceptable.

Fortunately, all powers forming the total received power are equallyaffected by the scale factor error γ(t) so when the noise rise ratioN_(R)(t) is calculated, the scale factor error is cancelled as

$\begin{matrix}\begin{matrix}{{N_{R}(t)} = {N_{R}^{{Digital}\mspace{14mu} {Receiver}}(t)}} \\{= \frac{P^{{Total},{{Digital}\mspace{14mu} {Receiver}}}(t)}{P_{N}^{{Digital}\mspace{14mu} {Receiver}}}} \\{= {\frac{{\gamma (t)}{P^{{Total},{Antenna}}(t)}}{{\gamma (t)}P_{N}^{Antenna}} =}} \\{= \frac{P^{{Total},{Antenna}}(t)}{P_{N}^{Antenna}}} \\{= {N_{R}^{Antenna}(t)}}\end{matrix} & (1)\end{matrix}$

where N_(R) ^(DigitalReceiver)(t) and N_(R) ^(Antenna)(t) are the noiserise ratios as measured at the digital receiver and at the antenna,respectively, P^(Total.DigitalReceiver)(t) and P^(Total.Antenna)(t) arethe total received powers at the digital receiver and the antenna,respectively, and P_(N) ^(DigitalReceiver) and P_(N) ^(Antenna) are thethermal noise level as measured at the digital receiver and the antenna,respectively. However, note that Equation (1) requires measurement ofthe noise floor P_(N) ^(DigitalReceiver) in the digital receiver.

Noise Floor

As indicated in the background section, the result of introducingadditional channels becomes an increase in the total power. Noise riseN_(R), defined as a ratio between a total power measure i.e. totalwideband power measure, and a thermal noise level P_(N) measured at anantenna connector, also referred to as the noise floor, is a measure ofthe load in the system. Above a noise rise threshold N_(R) ^(thr) thesituation may become unstable. A relation between a total bit rate and anoise rise is known from the design of power control loops andscheduling of additional channels can be performed once theinstantaneous noise rise N_(R) has been determined. The pole capacity,C_(pole) denotes the maximal bitrate capacity in bits per second. Atypical difference ΔN between the threshold N_(R) ^(thr) and the leveldefined by thermal noise level P_(N) is typically about 7-10 dB.However, the noise floor or thermal noise level P_(N) is not readilyavailable. For instance, since scale factor uncertainties in thereceiver may be as large as 2-3 dB as discussed above, a large part ofthe available margin is affected by such introduced uncertainties.

Observability of the Noise Floor

One reason for the difficulties to estimate the thermal noise floorpower now appears, since even if all measurements are made in thedigital receiver, the noise floor cannot be directly measured, at leastnot in a single RBS. The explanation is that neighbor cell interferenceand interference from external sources also affect the receiver, and anymean value of such sources cannot be separated from the noise floor.Power measurements in the own cell channels may in some cases beperformed. Such measurements do however not solve the entire problem,although they may improve the situation somewhat.

FIG. 1 illustrates various contributions to power measurements inconnection with an arbitrary radio base station (RBS) 20. The RBS 20 isassociated with a cell 30. Within the cell 30 a number of mobileterminals 25 are present, which communicate with the RBS 20 overdifferent links, each contributing to the total received power by P_(i)^(Code)(t). The cell 30 has a number of neighboring cells 31 within thesame WCDMA system, each associated with a respective RBS 21. Theneighboring cells 31 also comprise mobile terminals 26. The mobileterminals 26 emit radio frequency power and the sum of all contributionsis denoted by P^(N). There may also be other network external sources ofradiation, such as e.g. a radar station 41. P^(E) denotes contributionsfrom such external sources. Finally, the P_(N) term arises from thereceiver itself.

It is clear from the above that P^(N)(t) and P_(N) are not measurableand hence need to be estimated. The situation becomes even worse if onlymeasurements of total wide band power are available. Total wide bandpower measurements P_(Measurements) ^(Total)(t) can be expressedaccording to:

$\begin{matrix}{{P_{Measurements}^{total}(t)} = {{\sum\limits_{i = 1}^{n}{P_{i}^{Code}(t)}} + {P^{E + N}(t)} + {P_{N}(t)} + {^{Total}(t)}}} & (2)\end{matrix}$

where

P ^(E+N) =P ^(E) +P ^(N)   (3)

and where e^(Total)(t) models measurements noise.

It can be mathematically proven that a linear estimation of P^(E+N)(t)and P_(N) is not an observable entity. Only the quantityP^(E+N)(t)+P_(N) is observable from available measurements. This is trueeven in the case code power measurements are performed. The problem isthat there is no conventional technique that can be used to separate thenoise floor from power mean values originating from neighbor cellinterference and in-band interference sources external to the cellularsystem. Furthermore, if only measurements of total received wide bandpower is available, also the mean values of the individual code powercontributions are indistinguishable from the other contributions to thetotal power mean value.

Noise Floor Estimation

Yet another reason for the difficulty with noise rise estimation is thatthe thermal noise floor is not always the sought quantity. There aresituations where constant in-band interference significantly affects thereceiver of the RBS. These constant interferers do not affect thestability discussed above; they rather appear as an increased noisetemperature i.e. an increased thermal noise floor.

A possible solution is to use costly and individual determination of thethermal noise floor of each RBS in the field, in order to achieve a highenough load estimation performance. The establishment of the defaultvalue for the thermal noise power floor, as seen in the digital receiverrequires reference measurements performed over a large number of RBSseither in the factory or in the field. Both alternatives are costly andneed to be repeated as soon as the hardware changes.

The above approach to solve the problem would require calibration ofeach RBS individually. This would however be very costly and isextremely unattractive. Furthermore, temperature drift errors in theanalogue signal conditioning electronics of perhaps 0.7-1.0 dB wouldstill remain.

Another potential approach would be to provide an estimation of thethermal noise power floor. One principle for estimation of the thermalnoise power floor is to estimate it as a minimum of a measured orestimated power quantity comprising the thermal noise floor. Thisminimum is typically computed over a pre-determined interval in time. Ifno code power measurements are available, the power in question is thetotal received wideband power. One approach would therefore be tocalculate the noise rise as a division of the momentary total receivedwideband power with an established thermal noise floor power estimatedas a minimum of the total received wideband power over a pre-determinedinterval of time.

It is a well known fact that the thermal noise floor contribution alwaysis present, and consequently it can be concluded that if measurementuncertainties are neglected, the noise floor contribution has to beequal to or smaller than the minimum value of the total receivedwideband power received within a certain period of time. In essence, theminimum value of the total wideband power within a certain time intervalconstitutes an upper limit of the unknown noise floor.

A possible solution according to the above discussion could provide ahard algorithm for estimation of the thermal noise power floor, in thesense that a hard minimum value is computed over a sliding window, andused as an estimate of the thermal noise power floor. Consequently, thenoise floor could be determined as the minimum value (over a selectedinterval of time) of either of the following:

-   -   The sum of the power of the noise floor and the power of        neighbor and external interference.    -   The total received wideband power.

The noise rise is then subsequently calculated from one of the above twoquantities, by a division of the total received wideband power with theestablished thermal noise floor power.

With reference to FIG. 2, another possible solution provides a differentprinciple, based on soft estimation of the thermal noise power floor andthe noise rise. In the most advanced form, the possible noise riseestimation is performed in three main blocks 51, 52, 53.

The first block 51, i.e. power estimation block, applies a so calledKalman filter for estimation of certain power quantities that are neededby subsequent processing blocks. Specifically, the block 51 receives anumber of inputs 61A-E comprising the measured received total widebandpower (RTWP) 61A, measured code power to interference ratio (C/I) ofchannel i 61B, beta factors for channel i 61C, number of codes forchannel i 61D. corresponding code power to interference ratio commandedby a fast power control loop 61E, and provides outputs comprising powerestimates 62A, 62B and corresponding standard deviations 63A, 63B. Theoutput 62A is the estimate of a power quantity being the sum of neighborcell WCDMA interference power, in-band non-WCDMA interference power andthermal noise floor power, and the output 63A is the estimated receivedtotal wideband power and the output 63B is the corresponding variance.Together with the estimated means of said power quantities, thevariances of said power quantities define estimated PDF:s of said powerquantities (usually Gaussian in a preferred embodiment). Since theoutputs are from the Kalman filter arrangement, these parameters are theonly ones needed to define the estimated Gaussian distributions that areproduced by the filter. Thus, enough information is given to define theentire probability distribution information of the power estimates. Thescope of the present invention focuses on problems associated with thisblock 51.

The second block 52 applies Bayesian estimation techniques in order tocompute a conditional probability density function of the minimum of oneof the above mentioned power quantities. The minimum also accounts (byBayesian methods) for the prior distribution of the thermal noise powerfloor, thereby improving the average performance of the estimation, whenevaluated over an ensemble of RBSs. The actual value of the noise floorcan also be calculated by a calculation of the mean value of theestimated conditional probability distribution function. Specifically,the block 52 receives the power estimate 62A and the correspondingstandard deviations 62B as inputs, and provides an output 64 comprisingthe estimated probability distribution of an extreme value, typicallythe minimum, of P_(Estimate) ^(E+N+Noise), which is an estimate of thesum of neighbor cell interference power, external inband interferencepower and thermal noise power. Parameters 66 giving information about aprior expected probability distribution of the noise floor power isprovided to the conditional probability distribution estimation block52, in order to achieve an optimal estimation.

The third block 53 performs soft noise rise estimation by a calculationof the conditional probability distribution of the quotient of themomentary estimated wide band power probability distribution (from block52), and the conditional probability distribution of the noise powerfloor. The noise rise estimate is computed as a conditional mean. Thedetails are omitted here.

Complexity Reduced Kalman Filter

The most complicated setup of the previously described Kalman filterblock 51 estimates the time variable powers of each power controlledchannel of the cell, in order to allow a removal of own cell powerbefore the noise floor power is estimated by the following block 52. Theintention is that this reduction of interference, as seen by the thermalnoise floor estimation step in block 52, should improve the accuracy ofthe overall estimator.

A problem that immediately arises is that at least one state needs to bereserved for each channel, said state modelling the momentary channelpower. Since the computational complexity of a general Kalman filtervaries as the number of states raised to the third power, theconsequence of the above is an unacceptably high computationalcomplexity.

An alternative variant of the Kalman filtering method reduces thecomputational complexity by the introduction of an approximate blockstructure in several steps in the Kalman filter algorithm. The endachievement is a reduction of the computational complexity to the numberof states raised to the second power. This represents a substantialsaving, a factor of 25 in a typical situation.

In essence, with reference to FIG. 3, a modified version of thepreviously described possible method discloses using a simplified softsolution. Only the RTWP is measured and a simplified algorithm for RoTestimation is applied. The simplified algorithm accordingly applies asimplified, one-dimensional Kalman filter for estimation of the RTWP andthe corresponding variance. The reason why this filtering step is usedis that the subsequent (still soft) processing blocks requireprobability distributions as input. These are best generated by a Kalmanfilter in the first processing block, corresponding to block 51 of thepreviously described method.

Subsequently the thermal noise power floor is estimated with thecomplete soft algorithm, as described with reference to FIG. 2. Contraryto that previously described possible method, an (optimal) estimatedvalue of the thermal noise power floor is calculated. Finally, the lastprocessing block divides the estimated RTWP by the value of the thermalnoise power floor, to obtain an estimate of the RoT. Note that thisfinal step is not performed by a soft algorithm.

Sliding Window Solution in an Enhanced Uplink Scheduler

According to another variant in an enhanced uplink scheduler, a softsolution based on a single received wideband power (RTWP) measurement isused. Typically, the RTWP is measured and a simplified algorithm for RoTestimation is applied. The simplified algorithm described with referenceto FIG. 2 accordingly:

-   -   Applies a one-dimensional Kalman filter for estimation of the        RTWP and the corresponding variance. Hence, a special case of        the Kalman filter block is used.    -   Estimates the thermal noise power floor with the complete soft        algorithm, as initially described. Contrary to what is proposed        an (optimal) estimated value of the thermal noise power floor is        calculated.    -   The last processing block divides the estimated RTWP by the        value of the thermal noise power floor, to obtain an estimate of        the RoT. Note that this final step is not performed by a soft        algorithm.

Recursive Noise Floor Estimation for RNC Admission Control

An alternative variant relating to the second block 52 depicted in FIG.2. discloses a recursive formulation of a key equation and reduces thememory consumption of the noise floor estimation block to less than 1percent of previous requirements. This enables the execution of 1000+parallel instances of the soft noise rise estimation algorithm in theRNC.

Specific problems with the use of the above described previous variantsof the Kalman filter in the linear domain will be described more indetail below.

First Problem—Impaired Accuracy for Misaligned Prior Noise FloorInformation

Due to the use of a linear Kalman filter, as described with reference toblock 52 in FIG. 2, with parameter settings in the linear domain, itfollows that the estimated power quantities and the correspondingestimated covariances are the same, irrespective of the actual level ofthe power measurements. This is normally not a problem. However, whenthe dynamic range (or scaling) of the power inputs vary much, problemscan arise since the covariance becomes too small or too large, ascompared to the valid dynamic power levels.

The problem typically manifests itself in the second noise power floorestimation block 52 of the RoT estimator. Said second block 52 updatesthe probability distribution function of the minimum of severalestimated probability distribution functions (PDF) of said estimatedpower quantities. The PDF is represented as a histogram on a discretizedpower range or grid, typically between −120 dBm and −70 dBm. In order toavoid an excessive number of power grid points, the power grid islogarithmically distributed.

This means that more grid points are used for a given linear range forlow power levels, than for high power levels, cf. FIG. 4. This, in turn,results in PDF:s of said estimated power quantities that cover more gridpoints for a given linear range for low power levels, than for highpower levels. The effect of this is that the accuracy of the estimationstep of block 52 becomes dependent of the power level. Since knownalgorithms are tuned for a nominal power level, this introduces animpairment of the estimator accuracy, with regard to the noise powerfloor estimate.

For high power levels the effect is the contrary in that too few powergrid points are covered by the PDF:s of the power quantities, estimatedby block 51. Also, this effect impairs the accuracy of the noise powerfloor estimator. Details on the size of the accuracy impairment will bediscussed more in detail later.

Second Problem—Increased Computational Complexity for OverestimatedPrior Noise Floor Information

A consequence of the fact that the PDF:s of the power quantities usedfor computation of the PDF of the minimum of said power quantities covermore grid points when the power level is low, than when it is high, isthat also the computational complexity increases when the power level islow (as compared to the level used for tuning).

This fact is undesirable, since it introduces a need for additionalcomplexity margins in the algorithms, and also a need for specificsafety nets.

Hence, it would be desirable, according to the present invention, tointroduce methods that allow the widths, e.g. full width half maximum,of the PDF:s of said estimated power quantities to follow the meanmeasured power levels.

In order to solve the above mentioned problems, the basic idea of thepresent invention is to provide a suitable modification of the Kalmanfilter of block 51 of the RoT estimation algorithm, such that the keyestimated covariance of certain estimated power quantities used bysubsequent algorithmic steps, follow the measured mean power level. Inparticular, the covariance should be lower for power quantity levelsthat are lower than the nominal values used for tuning.

According to a basic embodiment of the present invention, the problem issolved by an explicit inclusion of a scaling of covariances e.g. theassumed measurement noise covariance and system noise covariance, or apre-computed covariance. The scaling is typically the same for these twoquantities. It is typically selected as the squared mean signal powerlevel.

As will be illustrated further on in this description, the effect ofthis scaling step leaves the Kalman filter gain, and hence the estimatedpower quantities unaffected. The corresponding covariances are howeverscaled, with the same scale factor, thereby resulting in said desiredsquared mean power level tracking. This solves the problems indicatedabove.

An embodiment of the present invention will be described with referenceto FIG. 6.

Accordingly, the present invention according to a basic embodiment,comprises measuring S0 one or more samples of the received totalwideband power (RTWP) and estimating S1 a probability distribution orprobability density function (PDF) for at least one power quantity orpower measure based on the measured RTWP. Subsequently, a mean oraverage power level is estimated S2 for the at least one power quantity,and finally the width of the estimated probability density is adapted S3based on the estimated mean power level. Thereby a probability densityfunction of a noise floor measure that is discretized on a grid isprovided.

In order to get a general treatment, valid for all variants of soft RoTestimation described above as well as for future variants, a generalextended Kalman filter formulation is used here. This is achieved by atreatment of a general state space model for description of the powersthat affect the cell load.

General State Space Power Model

The state space model that is used to describe the powers of the cellused in the noise rise estimator is

x(t+T)=A(t)x(t)+B(t)u(t)+w(t).

y(t)=c(x(t))+e(t)   (1)

Here x(t) is a state vector consisting of various powers of relevance toa specific cell, u(t) is an input vector consisting of certain powerreference values and commands, y(t) is an output vector consisting ofthe power measurements performed in the cell (e.g. the received totalwideband power, RTWP), w(t) is the so called systems noise thatrepresent the model error, and e(t) denotes the measurement error. Thematrix A(t) is the system matrix describing the dynamic modes, thematrix B(t) is the input gain matrix, while the vector c(x(t)) is the,possibly nonlinear, measurement vector which is a function of the statesof the system. Finally t represents the time and T represents thesampling period.

The General Extended Kalman Filter

In order to obtain a general discussion, a case with a nonlinearmeasurement vector is considered here. For this reason the extendedKalman filter, or variants thereof, needs to be applied, cf. [2]. Thisfilter is given by the following matrix and vector iterations,

$\begin{matrix}{{{C(t)} = {\left. \frac{\partial{c(x)}}{\partial x} \middle| {}_{x = {\hat{x}{({t|{t - T}})}}}{K_{f}(t)} \right. = {{P\left( t \middle| {t - T} \right)}{C^{T}(t)}\left( {{{C(t)}{P\left( t \middle| {t - T} \right)}{C^{T}(t)}} + {R_{2}(t)}} \right)^{- 1}}}}{{\hat{x}\left( t \middle| t \right)} = {{\hat{x}\left( t \middle| {t - T} \right)} + {{K_{f}(t)}\left( {{y(t)} - {{C(t)}{\hat{x}\left( t \middle| {t - T} \right)}}} \right)}}}{{P\left( t \middle| t \right)} = {{P\left( t \middle| {t - T} \right)} - {{K_{f}(t)}{C(t)}{P\left( t \middle| {t - T} \right)}}}}{{\hat{x}\left( {t + T} \middle| t \right)} = {{{Ax}\left( t \middle| t \right)} + {{Bu}(t)}}}{{P\left( {t + T} \middle| t \right)} = {{{{AP}\left( t \middle| t \right)}A^{T}} + R_{1}}}} & (2)\end{matrix}$

The quantities introduced by the filter iterations of Equations (2) areas follows. {circumflex over (x)}(t|t−T) denotes the state prediction,based on data up to time t−T, {circumflex over (x)}(t|t) denotes thefilter update, based on data up to time t, P(t|t−T) denotes thecovariance matrix of the state prediction, based on data up to time t−T,and P(t|t) denotes the covariance matrix of the filter update, based ondata up to time t. C(t) denotes the linearized measurement matrix(linearization around most current state prediction), K_(f)(t) denotesthe time variable Kalman gain matrix, R₂(t) denotes the measurementcovariance matrix, and R₁(t) denotes the system noise covariance matrix.It can be noted that R₁(t) and R₂(t) are often used as tuning variablesof the filter. In principle the bandwidth of the filter is controlled bythe matrix quotient of R₁(t) and R₂(t).

The Key Scaling Result

The algorithmic modifications of the present invention are based on thefollowing key results or observations by the inventor:

Result 1: Assume that solutions {circumflex over (x)}(t|t−T),{circumflex over (x)}(t|t), P(t|t−T) and P(t|t) are computed with theiteration (2). Assume then that R₁(t) and R₂(t) are re-scaled accordingto

R ₁ ^(α)(t)=α² R ₁(t)   (3)

R ₂ ^(α)(t)=α² R ₂(t),   (4)

and that the iterations are re-run with the same initialization. It thenfollows that the new solutions fulfill:

{circumflex over (x)} ^(α)(t|t−T)={circumflex over (x)}(t|t−T)   (5)

{circumflex over (x)} ^(α)(t|t)={circumflex over (x)}(t|t)   (6)

P ^(α)(t|t−T)=α² P(t|t−T)   (7)

P ^(α)(t|t)=α² P(t|t)   (8).

Proof: The first Equation of (2) is clearly unaffected by the scaling,by (5). Insertion of Equation (4) and Equation (7) into the secondEquation of (2) shows that also K_(f)(t) is unaffected by the scalingsince the original equation for the Kalman filter gain is obtained aftersimplification. This result, together with Equations (5) and (6) showthat the third equation of the initially described Kalman filter stillholds. In the same way it is shown that the scaling also disappears fromthe fourth equation of (2). The fifth equation of the original Kalmanfilter is obtained from Equations (4) and (5), whereas the previousresult for the fourth equation of (2), together with Equations (3), (7)and (8) show that also the final original equation of (2) is obtainedafter simplification. This proves the result, assuming that (2) isinitialized in the same way for both runs.

A few different embodiments of the present invention will be describedbelow.

Covariance Scaling

According to a specific embodiment of the present invention and based onthe discussion above, it is possible to obtain an automatic adjustmentof the covariance matrix P(t|t) to one measured mean power level, byintroduction of a similar scaling of R₁(t) and R₂(t). Here α²corresponds to the mean squared power of a suitable power quantity,divided by the mean squared power of the same suitable power quantity atthe nominal power level where R₁(t) and R₂(t) are tuned.

In this variant of the scaling algorithm according to the invention, the(extended) Kalman filter is run as usual, together with a scale factorestimation) and corresponding online correction to (2).

Output Scaling

In another specific embodiment, a simplified approach is possible. Thisembodiment is particularly applicable for time invariant, linear cases,in which the asymptotic Kalman filter gain K_(f)(∞) and the covariancesP(∞|∞−T) and P(∞|∞) can be pre-computed, thereby reducing thecomputational complexity considerably. The scaling of the presentinvention would in such cases be directly applied to the pre-computedcovariance, i.e as

P ^(α)(t|t)=α²(t)P(∞|∞).   (9)

Note that a time variable scale factor has been introduced to stress thefact that the scale factor is normally tracked.

The scaling parameter or scaling factor a(t) of the present inventioncan be estimated or determined in different ways, an embodiment ofestimating or generating a scale parameter or factor will be describedin more detail below.

A typical approach would, according to a specific embodiment, be toestimate the scale factor by applying a recursive averaging filter to asuitable measured power quantity. If this is done in the linear domain,poor performance can result though. This is due to the very largedynamic range variations of up to 50 dB that may occur.

As an example consider a case where the recursive averaging filter has atime constant corresponding to 1000 power samples. In case the filterhas settled at a low power level, a sudden increase of the power levelwith e.g. 30 dB means that the latest power sample would dominate overthe state of the filter, causing convergence to the new level 30 dBabove the lower level within a few power samples.

On the contrary, in case the filter would experience a power level dropof 30 dBs, it would take about 1000 samples before the filter state hasdropped to 37% of its initial value. The time required to reach a level30 dBs below the original level far exceeds 1000 samples.

Hence the conclusion is that when large changes in level occurs,averaging in the linear domain causes the response of the averagingfilter to be un-symmetric on rising and falling edges of the powerlevel.

A remedy for the above situation is to perform the averaging of thepresent invention in the logarithmic domain, i.e. apply the recursiveaveraging filter to values expressed in dBW or dBm.

An embodiment of the complete scale factor adaptation according to theinvention can then be expressed as follows, for a simple first orderrecursive case:

$\begin{matrix}{{x_{\log}(t)} = {10\; {\log^{10}\left( {x(t)} \right)}}} & (10) \\{{{\overset{\_}{x}}_{\log}\left( {t + T} \right)} = {{k\; {{\overset{\_}{x}}_{\log}(t)}} + {\left( {1 - k} \right){x_{\log}(t)}}}} & (11) \\{{\alpha (t)} = {\frac{10^{(\frac{{\overset{\_}{x}}_{\log}{(t)}}{10})}}{x_{nominal}}.}} & (12)\end{matrix}$

Above x(t) is a measured power quantity in the linear domain, x_(log)(t)is the corresponding value in the logarithmic domain, x _(log)(t) is thelogarithmic mean power value of the recursive filter at time t andt_(nominal) is the nominal value of the power level (used for tuning) inthe linear domain.

Finally, note that the covariances, according to the invention,typically scale like α². The values needed in the distributions arenormally the standard deviations that then scale like |α|.

To further illustrate the advantages of the present invention, a fewnumerical examples in the form of comparative simulations will bedescribed in the following, with reference to FIG. 7-FIG. 9. Thenumerical examples below illustrate:

-   -   The reduced accuracy without the present invention: and    -   The correction that restores the accuracy, said correction being        obtained with the present invention.

A simulation was performed with a known algorithm, with and without theembodiments of the scaling of the present invention. FIG. 7 illustratesthe measured total received wideband power (RTWP), FIG. 8 illustratesthe estimated noise power floor without the present invention, whileFIG. 9 illustrates the estimated noise power floor based on the presentinvention.

It can be seen from FIG. 7 that the true noise power level is about−116.0 dBm, while the algorithm is tuned for a nominal level of −104dBm. The algorithm without scaling delivers a noise floor about 1 dB toolow, as compared to the algorithm with scaling that is almost on target.

It should be noted that a 1 dB error means that an equivalent marginneeds to be introduced e.g. in the admission control or enhanced uplinkfunctions. This directly translates to a corresponding capacity loss (inthis case about 5 voice calls).

FIG. 10 illustrates main parts of an embodiment of a system according tothe present invention. A wireless communications system 70 comprises aUniversal mobile telecommunication system Terrestrial Radio AccessNetwork (UTRAN) 71. A mobile terminal 25 is in radio contact with a RBS20 in the UTRAN 71. The RBS 20 is controlled by a Radio NetworkController (RNC) 72, which in turn is connected to a Mobile servicesSwitching Centre/Visitor Location Register (MSC/VLR) 74 and a ServingGeneral packet radio system Support Node (SGSN) 75 of a core network CN73.

In this embodiment, the RBS 20 comprises means for obtainingmeasurements 80 of samples of at least the received total widebandpower, means for estimating 81 a probability distribution of a firstpower quantity based on the measured total wideband power. The RBS 20further comprises means for estimating 82 a mean power level for thefirst power quantity, and means for adapting 83 a width of the estimatedprobability distribution, to enable a subsequent calculation of aprobability density function of a noise floor measure that isdiscretized on a grid.

In addition, and according to an alternative embodiment, the systemfurther comprises an estimator unit for estimating or determining ascaling parameter α(t) to be supplied as an input parameter to theadaptation unit 83.

The different means 80-83 can according to further embodiments belocated within the RBS 20, as discussed above, or the RNC 72 or a mobileor user terminal 25. In the latter case, the invention concerns downlinknoise floor estimation. This is indicated by the dashed boxes in the RNC72 and the user terminal 25.

In addition to the above described features, the RNC 22 can according toknown measures comprise means 84 for admission control. The means 84 foradmission control comprises preferably functionality for enhanced uplinkcontrol, and is connected to the RBS 20 for information exchange, inparticular concerning noise rise estimates.

Advantages of the present invention comprise

-   -   A significantly enhanced accuracy of the thermal noise power        floor estimation step of soft noise rise estimation algorithms.        Note that the invention applies to any present or future soft        noise rise estimation algorithms, including:        -   The first described general algorithm.        -   The sliding window algorithm        -   The recursive algorithm        -   Any future algorithm whose dynamic Kalman filter estimation            part is based on the model of Equations (1)-(2).    -   A computational complexity that does not grow in cases where the        nominal mean power level is underestimated.

The embodiments described above are to be understood as a fewillustrative examples of the present invention. It will be understood bythose skilled in the art that various modifications, combinations, andchanges may be made to the embodiments without departing from the scopeof the present invention. In particular, different part solutions in thedifferent embodiments can be combined in other configurations, wheretechnically possible. However, the scope of the present invention isdefined by the appended claims.

REFERENCES

[1] H. Holma and A. Toskala, WCDMA for UMTS—Radio Access for ThirdGeneration Mobile Communications. Chichester, UK: Wiley, 2000.

[2] T. Söderström, Discrete Time Stochastic Systems. London, UK:Springer. 2002, pp. 12-14, 123-126, 142, 149-150, 247.

1. A method of enabling improved soft noise power floor estimation in acode division multiple access wireless communication system, said methodcomprising the steps of: measuring samples of at least received totalwideband power; estimating a probability distribution for a first powerquantity from at least said measured received total wideband power;estimating a mean power level for said first power quantity; and,adapting a width of said probability distribution based at least on saidestimated mean power level to enable computation of a probabilitydensity function of a noise floor measure that is discretized on a grid.2. The method according to claim 1, wherein said grid is a power grid.3. The method according to claim 1, wherein said grid is logarithmic. 4.The method according to claim 1, wherein said step of adapting providesa width of said probability distribution of first power quantity thatfollows the discretization density of said grid.
 5. The method accordingto claim 1, wherein said step of adapting comprises the further step ofdetermining a scaling parameter a(t) based on said estimated mean powerlevel.
 6. The method according to claim 5, wherein said step of adaptingcomprises scaling at least one covariance for said first power quantitybased on said scaling parameter.
 7. The method according to claim 6,wherein said at least one covariance is an estimated covariance.
 8. Themethod according to claim 6, wherein said at least one covariancecomprises an assumed measurement noise covariance and a system noisecovariance.
 9. The method according to claim 8, wherein both saidcovariances are scaled with the same scaling parameter.
 10. The methodaccording to claim 5, wherein said step of adapting comprises scaling apre-computed covariance, said scaling is based on said scalingparameter.
 11. The method according to claim 5, wherein said scaling isperformed based on said scaling parameter squared α²(t).
 12. The methodaccording to claim 5, wherein said scaling parameter is determined bymeans of averaging over a sliding window.
 13. The method according toclaim 6, wherein said scaling parameter α(t) is determined according to:$\begin{matrix}{{x_{\log}(t)} = {10\; {\log^{10}\left( {x(t)} \right)}}} \\{{{\overset{\_}{x}}_{\log}\left( {t + T} \right)} = {{k\; {{\overset{\_}{x}}_{\log}(t)}} + {\left( {1 - k} \right){x_{\log}(t)}}}} \\{{\alpha (t)} = {\frac{10^{(\frac{{\overset{\_}{x}}_{\log}{(t)}}{10})}}{x_{nominal}}.}}\end{matrix}$ where x(t) is a measured power quantity in the lineardomain, x_(log)(t) is a corresponding value in the logarithmic domain, x_(log)(t) is the logarithmic mean power value of the recursive filter attime t and x_(nominal) is the nominal value of the power level in thelinear domain.
 14. The method according to claim 6, wherein said scalingof said at least one covariance is performed according to:R ₁ ^(α)(t)=α²(t)R ₁(t)R ₂ ^(α)(t)=α²(t)R ₂(t) where R₁ ^(α)(t) is the scaled system noisecovariance matrix R₁(t), R₂ ^(α)(t) is the scaled measurement covariancematrix R₂(t), and α²(t) is the squared scaling parameter.
 15. The methodaccording to claim 7, wherein said scaling is performed according to:P ^(α)(t|t)=α²(t)P(∞|∞) where P^(α)(t|t) is the scaled pre-computedcovariance matrix, α²(t) is the squared scaling parameter, and P(∞|∞) isthe pre-computed covariance matrix.
 16. The method according to claim 1,wherein said width of said probability distribution is the Full Width atHalf Maximum of a Gaussian distribution.
 17. A node in a code divisionmultiple access wireless communication system, said node comprising:means for measuring samples of at least received total wideband power;means for estimating a probability distribution for a first powerquantity from at least said measured received total wideband power;means for estimating a mean power level for said first power quantity;and, means for adapting a width of said probability distribution basedat least on said estimated mean power level, to enable computation of aprobability density function of a noise floor measure that isdiscretized on a grid.
 18. The node according to claim 17, wherein saidgrid is a power grid.
 19. The node according to claim 17, wherein saidgrid is logarithmic.
 20. The node according to claim 17, wherein saidmeans for adapting are operative to provide a width of said probabilitydistribution of first power quantity that follows the discretizationdensity of said grid.
 21. The node according to claim 17, wherein saidmeans for adapting are further operative to determine a scalingparameter a(t) based on said estimated mean power level.
 22. The nodeaccording to claim 21, wherein said means for adapting are arranged toscale at least one covariance for said first power quantity based onsaid scaling parameter.
 23. The node according to claim 22, wherein saidat least one covariance is an estimated covariance.
 24. The nodeaccording to claim 22, wherein said at least one covariance comprises anassumed measurement noise covariance and a system noise covariance. 25.The node according to claim 24, wherein both said covariances are scaledwith the same scaling parameter.
 26. The node according to claim 21,wherein said adaptation means are arranged to scale a pre-computedcovariance, said scaling is based on said scaling parameter.
 27. Thenode according to claim 21, wherein said scaling is performed based onsaid scaling parameter squared α²(t).
 28. The node according to claim21, wherein said scaling parameter is determined by means of averagingover a sliding window.
 29. The node according to claim 17, wherein saidnode is a mobile terminal.
 30. The node according to claim 17, whereinsaid system is a WCDMA system.